{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cb658e80",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a href=\"https://colab.research.google.com/github/nyandwi/machine_learning_complete/blob/main/6_classical_machine_learning_with_scikit-learn/7_random_forests_for_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e2bf300",
   "metadata": {},
   "source": [
    "*This notebook was created by [Jean de Dieu Nyandwi](https://twitter.com/jeande_d) for the love of machine learning community. For any feedback, errors or suggestion, he can be reached on email (johnjw7084 at gmail dot com), [Twitter](https://twitter.com/jeande_d), or [LinkedIn](https://linkedin.com/in/nyandwi).*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c5ba4fe",
   "metadata": {},
   "source": [
    "<a name='0'></a>\n",
    "# Random Forests - Intro and Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07494ffb",
   "metadata": {},
   "source": [
    "Random Forests are powerful machine learning algorithms used for supervised classification and regression. Random forests works by averaging the predictions of the multiple and randomized decision trees. Decision trees tends to overfit and so by combining multiple decision trees, the effect of overfitting can be minimized. \n",
    "\n",
    "Random Forests are type of ensemble models. More about ensembles models in the next notebook. \n",
    "\n",
    "Different to other learning algorithms, random forests provide a way to find the importance of each feature and this is implemented in Sklearn. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4e2c5a6",
   "metadata": {},
   "source": [
    "## Random Forests for Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae5e8372",
   "metadata": {},
   "source": [
    "### Contents\n",
    "\n",
    "* [1 - Imports](#1)\n",
    "* [2 - Loading the data](#2)\n",
    "* [3 - Exploratory Analysis](#3)\n",
    "* [4 - Preprocessing the data](#4)\n",
    "* [5 - Training Random Forests Regressor](#5)\n",
    "* [6 - Evaluating Random Forests Regressor](#6)\n",
    "* [7 - Improving Random Forests Regressor](#7)\n",
    "* [8 - Feature Importance](#8)\n",
    "* [9 - Evaluating the Improved Model on the Test set](#9)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99058d12",
   "metadata": {},
   "source": [
    "<a name='1'></a>\n",
    "## 1 - Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "df278c5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35699c38",
   "metadata": {},
   "source": [
    "<a name='2'></a>\n",
    "\n",
    "## 2 - Loading the data\n",
    "\n",
    "In this regression task with random forests, we will use the same dataset previously used in deciosion trees regressor which is Machine CPU (Central Processing Unit) data which is available at [OpenML](https://www.openml.org/t/5492). We will load it with Sklearn `fetch_openml` function. \n",
    "\n",
    "If you are reading this, it's very likely that you know CPU or you have once(or many times) thought about it when you were buying your computer. In this notebook, we will predict the relative performance of the CPU given the following data: \n",
    "\n",
    "* MYCT: machine cycle time in nanoseconds (integer)\n",
    "* MMIN: minimum main memory in kilobytes (integer)\n",
    "* MMAX: maximum main memory in kilobytes (integer)\n",
    "* CACH: cache memory in kilobytes (integer)\n",
    "* CHMIN: minimum channels in units (integer)\n",
    "* CHMAX: maximum channels in units (integer)\n",
    "* PRP: published relative performance (integer) (target variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "67905ee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's hide warnings\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d3083946",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "\n",
    "machine_cpu = fetch_openml(name='machine_cpu', version=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "46c09852",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.utils.Bunch"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(machine_cpu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8689a0ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': '230',\n",
       " 'name': 'machine_cpu',\n",
       " 'version': '1',\n",
       " 'description_version': '1',\n",
       " 'format': 'ARFF',\n",
       " 'contributor': 'L. Torgo',\n",
       " 'upload_date': '2014-04-23T13:20:36',\n",
       " 'language': 'English',\n",
       " 'licence': 'Public',\n",
       " 'url': 'https://www.openml.org/data/v1/download/3667/machine_cpu.arff',\n",
       " 'file_id': '3667',\n",
       " 'default_target_attribute': 'class',\n",
       " 'version_label': '1',\n",
       " 'citation': 'https://archive.ics.uci.edu/ml/citation_policy.html',\n",
       " 'tag': 'OpenML-Reg19',\n",
       " 'visibility': 'public',\n",
       " 'original_data_url': 'http://www.ics.uci.edu/~mlearn/MLSummary.html',\n",
       " 'minio_url': 'http://openml1.win.tue.nl/dataset230/dataset_230.pq',\n",
       " 'status': 'active',\n",
       " 'processing_date': '2020-11-20 19:15:43',\n",
       " 'md5_checksum': 'e26d62e83069b74dff6cf492e06868a0'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "machine_cpu.details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "154ed67e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(209, 6)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "machine_cpu.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "69e438fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**Author**:   \n",
      "**Source**: Unknown -   \n",
      "**Please cite**:   \n",
      "\n",
      "The problem concerns Relative CPU Performance Data. More information can be obtained in the UCI Machine\n",
      " Learning repository (http://www.ics.uci.edu/~mlearn/MLSummary.html).\n",
      " The used attributes are :\n",
      " MYCT: machine cycle time in nanoseconds (integer)\n",
      " MMIN: minimum main memory in kilobytes (integer)\n",
      " MMAX: maximum main memory in kilobytes (integer)\n",
      " CACH: cache memory in kilobytes (integer)\n",
      " CHMIN: minimum channels in units (integer)\n",
      " CHMAX: maximum channels in units (integer)\n",
      " PRP: published relative performance (integer) (target variable)\n",
      " \n",
      " Original source: UCI machine learning repository. \n",
      " Source: collection of regression datasets by Luis Torgo (ltorgo@ncc.up.pt) at\n",
      " http://www.ncc.up.pt/~ltorgo/Regression/DataSets.html\n",
      " Characteristics: 209 cases; 6 continuous variables\n",
      "\n",
      "Downloaded from openml.org.\n"
     ]
    }
   ],
   "source": [
    "print(machine_cpu.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d7180991",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MYCT', 'MMIN', 'MMAX', 'CACH', 'CHMIN', 'CHMAX']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying feature names\n",
    "\n",
    "machine_cpu.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e3ba092e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the whole dataframe\n",
    "\n",
    "machine_data = machine_cpu.frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ef5bf335",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(machine_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c492fd93",
   "metadata": {},
   "source": [
    "<a name='3'></a>\n",
    "## 3 - Exploratory Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9906083",
   "metadata": {},
   "source": [
    "Before doing exploratory analysis, let's get the training and test data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "98d74144",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The size of training data is: 167 \n",
      "The size of testing data is: 42\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_data, test_data = train_test_split(machine_data, test_size=0.2,random_state=20)\n",
    "\n",
    "print('The size of training data is: {} \\nThe size of testing data is: {}'.format(len(train_data), len(test_data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3fc640e",
   "metadata": {},
   "source": [
    "Let's visualize the histograms of all numeric features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "04856278",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data.hist(bins=50, figsize=(15,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eca7747",
   "metadata": {},
   "source": [
    "Or we can quickly use `sns.pairplot()` to look into the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "803a1d12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7faa5972d890>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1260x1260 with 56 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "753bb539",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>207.958084</td>\n",
       "      <td>2900.826347</td>\n",
       "      <td>11761.161677</td>\n",
       "      <td>26.071856</td>\n",
       "      <td>4.760479</td>\n",
       "      <td>18.616766</td>\n",
       "      <td>109.185629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>266.772823</td>\n",
       "      <td>4165.950964</td>\n",
       "      <td>12108.332354</td>\n",
       "      <td>42.410014</td>\n",
       "      <td>6.487439</td>\n",
       "      <td>27.489919</td>\n",
       "      <td>174.061117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>27.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>110.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>232.500000</td>\n",
       "      <td>3100.000000</td>\n",
       "      <td>16000.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>110.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1500.000000</td>\n",
       "      <td>32000.000000</td>\n",
       "      <td>64000.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>1150.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              MYCT          MMIN          MMAX        CACH       CHMIN  \\\n",
       "count   167.000000    167.000000    167.000000  167.000000  167.000000   \n",
       "mean    207.958084   2900.826347  11761.161677   26.071856    4.760479   \n",
       "std     266.772823   4165.950964  12108.332354   42.410014    6.487439   \n",
       "min      17.000000     64.000000     64.000000    0.000000    0.000000   \n",
       "25%      50.000000    768.000000   4000.000000    0.000000    1.000000   \n",
       "50%     110.000000   2000.000000   8000.000000    8.000000    2.000000   \n",
       "75%     232.500000   3100.000000  16000.000000   32.000000    6.000000   \n",
       "max    1500.000000  32000.000000  64000.000000  256.000000   52.000000   \n",
       "\n",
       "            CHMAX        class  \n",
       "count  167.000000   167.000000  \n",
       "mean    18.616766   109.185629  \n",
       "std     27.489919   174.061117  \n",
       "min      0.000000     6.000000  \n",
       "25%      5.000000    27.500000  \n",
       "50%      8.000000    50.000000  \n",
       "75%     24.000000   110.000000  \n",
       "max    176.000000  1150.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking summary stats\n",
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f0e4fb67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MYCT     0\n",
       "MMIN     0\n",
       "MMAX     0\n",
       "CACH     0\n",
       "CHMIN    0\n",
       "CHMAX    0\n",
       "class    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking missing values\n",
    "\n",
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4977a1a1",
   "metadata": {},
   "source": [
    "We don't have any missing values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1da3a3f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MYCT    -0.301805\n",
       "MMIN     0.797751\n",
       "MMAX     0.869077\n",
       "CACH     0.671581\n",
       "CHMIN    0.648653\n",
       "CHMAX    0.606557\n",
       "class    1.000000\n",
       "Name: class, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking feature correlation\n",
    "\n",
    "corr = train_data.corr()\n",
    "corr['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0e24c4e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Visualizing correlation\n",
    "\n",
    "plt.figure(figsize=(12,7))\n",
    "\n",
    "sns.heatmap(corr,annot=True,cmap='crest')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6733727",
   "metadata": {},
   "source": [
    "<a name='4'></a>\n",
    "\n",
    "## 4 - Data Preprocessing \n",
    "\n",
    "It is here that we prepare the data to be in the proper format for the machine learning model. \n",
    "\n",
    "Let's set up a pipeline to scale features but before that, let's take training input data and labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "acb7a81e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train_data.drop('class', axis=1)\n",
    "y_train = train_data['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b7ee6d06",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "scale_pipe = Pipeline([\n",
    "    ('scaler', StandardScaler())\n",
    "    \n",
    "])\n",
    "\n",
    "X_train_scaled = scale_pipe.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd496de6",
   "metadata": {},
   "source": [
    "<a name='5'></a>\n",
    "\n",
    "## 5 - Training Random Forests Regressor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8311b87a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=False, n_jobs=-1, random_state=42)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor(min_samples_split=2,bootstrap=False, random_state=42,n_jobs=-1)\n",
    "\n",
    "forest_reg.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9afb9bde",
   "metadata": {},
   "source": [
    "<a name='6'></a>\n",
    "\n",
    "## 6 - Evaluating Random Forests Regressor\n",
    "\n",
    "Let's first check the root mean squarred errr on the training. It is not advised to evaluate the model on the test data since we haven't improved it yet. I will make a function to make it easier and to avoid repetitions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6962359e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "def predict(input_data,model,labels):\n",
    "    \"\"\"\n",
    "    Take the input data, model and labels and return predictions\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    preds = model.predict(input_data)\n",
    "    mse = mean_squared_error(labels,preds)\n",
    "    rmse = np.sqrt(mse)\n",
    "    rmse\n",
    "    \n",
    "    return rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a17fed16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.724590719956222"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict(X_train_scaled, forest_reg, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66a574a5",
   "metadata": {},
   "source": [
    "<a name='7'></a>\n",
    "\n",
    "## 7 - Improving Random Forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0f92566d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': False,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'criterion': 'mse',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'auto',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_impurity_split': None,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': -1,\n",
       " 'oob_score': False,\n",
       " 'random_state': 42,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_reg.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af110f75",
   "metadata": {},
   "source": [
    "We will use GridSearch to find the best hyperparameters that we can use to retrain the model with. By setting the `refit` to `True`, the random forest will be automatically retrained on the dataset with the best hyperparameters. By default, `refit` is True.\n",
    "\n",
    "This will take too long. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "056818ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 250 candidates, totalling 1250 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestRegressor(bootstrap=False, random_state=42),\n",
       "             param_grid={'max_leaf_nodes': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,\n",
       "                                            11, 12, 13, 14, 15, 16, 17, 18, 19,\n",
       "                                            20, 21, 22, 23, 24, 25, 26, 27, 28,\n",
       "                                            29, ...],\n",
       "                         'n_estimators': [100, 200, 300, 400, 500]},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "params_grid = {\n",
    "    'n_estimators':[100,200,300,400,500],\n",
    "    'max_leaf_nodes':list(range(0,50))}\n",
    "\n",
    "#refit is true by default. The best estimator is trained on the whole dataset \n",
    "\n",
    "grid_search = GridSearchCV(RandomForestRegressor(min_samples_split=2,bootstrap=False,random_state=42), params_grid, verbose=1, cv=5)\n",
    "\n",
    "grid_search.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "55a5d7f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_leaf_nodes': 42, 'n_estimators': 200}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c4a1488d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=False, max_leaf_nodes=42, n_estimators=200,\n",
       "                      random_state=42)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4995ab2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "forest_best = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44fc64ef",
   "metadata": {},
   "source": [
    "Let's make prediction on the training data again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "85b1b211",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.709506767466658"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict(X_train_scaled, forest_best, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40fb17de",
   "metadata": {},
   "source": [
    "Surprisingly, by searching model hyperparameters, the model did not improve. Can you guess why? I have observed many things while running Grid Search and reading about the [random forests](https://scikit-learn.org/stable/modules/ensemble.html#forests-of-randomized-trees). If you can't get good results, set the `bootstrap` to False. It is true by default, and that means that you are training on samples of the training set instead of the whole training set. Try going back to the orginal model and change it to True and note how the prediction changes. Also learn more about the other hyperparameters. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77ed447f",
   "metadata": {},
   "source": [
    "<a name='8'></a>\n",
    "## 8. Feature Importance "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a1e9c1f",
   "metadata": {},
   "source": [
    "Different to other machine learning models, random forests can show how each feature contributed to the model generalization. Let's find it.\n",
    "The results are values between 0 and 1. The closer to 1, the good the feature was to the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "92298569",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>Feature Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MYCT</td>\n",
       "      <td>0.002991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MMIN</td>\n",
       "      <td>0.005537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MMAX</td>\n",
       "      <td>0.835814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CACH</td>\n",
       "      <td>0.117431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHMIN</td>\n",
       "      <td>0.007570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CHMAX</td>\n",
       "      <td>0.030657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Features  Feature Importance\n",
       "0     MYCT            0.002991\n",
       "1     MMIN            0.005537\n",
       "2     MMAX            0.835814\n",
       "3     CACH            0.117431\n",
       "4    CHMIN            0.007570\n",
       "5    CHMAX            0.030657"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_import = forest_best.feature_importances_\n",
    "\n",
    "feat_dict ={\n",
    "    \n",
    "    'Features': X_train.columns,\n",
    "    'Feature Importance': feat_import\n",
    "}\n",
    "\n",
    "pd.DataFrame(feat_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a91ab967",
   "metadata": {},
   "source": [
    "As you can see above, the most 2 features which contributed to the prediction of the relative performance of the CPU are `MMAX` which is the Maximum Main Memory in Kilobytes and `CACH` (cache memory). \n",
    "\n",
    "It makes sense that the model was able to find that out. Main memory (RAM, Read Only Memory) and cache memory (which stores frequently used information thus facilitating faster processing and quick retrieval of information) are the two most factors of the CPU performance and if you are going to buy a new computer, you want to have high RAM and cache memory in order to have a powerful machine that can process/compute and retrieve things faster. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5adbd33",
   "metadata": {},
   "source": [
    "<a name='9'></a>\n",
    "## 9. Evaluating the Model on the Test Set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4f82117",
   "metadata": {},
   "source": [
    "Let us evaluate the model on the test set. But I will first run the pipeline on the test data. Note that we only transform (not fit_transform)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "07f6ff2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = test_data.drop('class', axis=1)\n",
    "y_test = test_data['class']\n",
    "\n",
    "test_scaled = scale_pipe.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f03cc7fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41.35371179215193"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict(test_scaled, forest_best, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8136b43",
   "metadata": {},
   "source": [
    "The results on the test set is not appealing, and it is a sign that the model is still overfitting the data(it is doing well on the training set and poor on the new data). One way to improve the model can be to regularize the model by searching more best hyperparameters and increasing the data and data quality. The later is what can improve the model in many scenarios. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af266a08",
   "metadata": {},
   "source": [
    "This is the end of the notebook. We have learned the fundamental idea behind the random forests, and used it to predict the CPU performance. In the next lab, we will use it for classification task and we will use a real world dataset so that we can practically improve the random forests."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17af5728",
   "metadata": {},
   "source": [
    "[BACK TO TOP](#0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "620a4d3e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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